Paper 2018/476

The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations

Stjepan Picek, Annelie Heuser, Alan Jovic, Shivam Bhasin, and Francesco Regazzoni


We concentrate on machine learning techniques used for profiled side-channel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight or Hamming distance leakage models. In order to deal with the imbalanced data, we use various balancing techniques and we show that most of them help in mounting successful attacks when the data is highly imbalanced. Especially, the results with the SMOTE technique are encouraging, since we observe some scenarios where it reduces the number of necessary measurements more than 8 times. Next, we provide extensive results on comparison of machine learning and side-channel metrics, where we show that machine learning metrics (and especially accuracy as the most often used one) can be extremely deceptive. This finding opens a need to revisit the previous works and their results in order to properly assess the performance of machine learning in side-channel analysis.

Available format(s)
Publication info
Preprint. MINOR revision.
Profiled side-channel attacksImbalanced datasetsSynthetic examplesSMOTEMetrics
Contact author(s)
picek stjepan @ gmail com
2018-11-12: revised
2018-05-23: received
See all versions
Short URL
Creative Commons Attribution


      author = {Stjepan Picek and Annelie Heuser and Alan Jovic and Shivam Bhasin and Francesco Regazzoni},
      title = {The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations},
      howpublished = {Cryptology ePrint Archive, Paper 2018/476},
      year = {2018},
      note = {\url{}},
      url = {}
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